From: Gehua Yang <yangg2@rp...>  20050510 22:16:30

> > maziyar wrote: > >> I am using a 16x3 matrix. The data is the following: >> >> 78.317 81.539 85.283 >> 134.87 135.07 136.71 >> 137.84 136.2 147.03 >> 83.278 82.835 93.192 >> 87.246 86.773 80.599 >> 143.79 138.87 132.94 >> 147.76 147.82 141.45 >> 91.214 95.244 91.233 >> 150.22 144.2 154.16 >> 140.3 141.93 136.84 >> 172.05 171.58 164.5 >> 179.99 174.78 180.63 >> 190.9 198.19 196.92 >> 182.96 181.72 182.31 >> 212.72 211.98 210.58 >> 220.66 225.64 228.5 >> >> This data is yielding me different answers. Thus, as per my earlier >> email, I am transposing this then finding the covariance matrix. >> Then the resulting eigenvector produced using the VNL libraries >> differs from the results produced in matlab. >> Besides Kang's comments, a couple thoughts: You mentioned that this 16x3 matrix is a covariance matrix. However, as covariance matrix is defined as Covar(x) = E[ (xmean_x)(xmean_x)^T ], which is always square and symmetric, you may want doublecheck it. On the other hand, to decompose this 16x3 matrix, Singular Value Decomposition(SVD) is more desired. Gehua 